Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet

A general linear model (GLM) was used to standardize catch per unit effort (CPUE) data for Alaska walleye pollock (Theragra chalcogramma) from the Bering Sea fleet for the years 1995–1999. Data were stratified temporally by year and season and spatially by area using either Alaska Department of Fish and Game (ADF&G) or National Marine Fisheries Service (NMFS) reporting areas. Four factors were used: vessel identification (ID) number, vessel speed, percentage of pollock by weight in the haul (a measure of targeting), and whether most of the haul took place before or after sunset. At least 29 combinations of main effects, quadratic covariates, and interactions were tested for each year/area/season stratum. GLM models explained from 31 to 48% of the total sums of squares. Vessel identification number was included in all models and explained the most variability. Of the remaining factors, the square of the percentage of pollock in the haul was included in most models, following an F-test to determine parsimony. Analysis of the vessel identification number coefficients indicated that larger vessels tended to have higher CPUEs; and that this relationship differed between dedicated catcher vessels and offshore catcher processors. Coefficient estimates and response surfaces generally indicated increased CPUEs with the percentage of pollock in the haul and showed mixed results with vessel speed. The vessel identification number incorporated most vessel characteristics, leaving vessel speed primarily as a fitting variable with less biological meaning. The year/area/season stratification procedure was found to be necessary due to the unbalanced design, which otherwise would have factor levels with no data in a large combined model. In addition, the stratification procedure reduced the variability in CPUE substantially.     

钢质海洋渔政船空船重量与重心估算方法研究

在实船资料统计分析和基于船舶基本原理基础上,提出了适合渔政船设计初期空船重量及重心的估算公式,以此分析、整理出的回归方程更具有针对性。将空船重量、重心高度、重心纵向位置的公式计算结果与实船倾斜试验所得值相比较后发现,相对误差符合工程要求,重心纵向位置值亦在允许范围内。本估算方法可以给设计者以前期估算参考。     

基于线性混合模型的蓝点马鲛叉长和体重关系的月间及性别差异

为了研究蓝点马鲛（）生活史特征的异质性,根据2018年10月至2019年3月在东海外海渔场的拖网调查采样数据,对其叉长和体重关系的月间及性别差异进行了研究。依据收集的367尾蓝点马鲛样本,求得其叉长和体重关系（b的估计均值为2.794。本研究构建了广义线性模型和9个线性混合模型,用于研究蓝点马鲛的叉长和体重关系（）在时间及性别上的差异。贝叶斯信息准则（BIC）值和均方根误差值均表明,最复杂的线性混合模型（即月份和性别对两个参数P<0.01）。在最优模型中,a值则与此相反。本研究表明,月份和性别对蓝点马鲛叉长和体重关系具有显著的影响,线性混合模型能把月份和性别的异质性通过随机效应在单个模型中更准确、快速地体现,从而进一步证实了此模型在数据来源异质性研究中的优势。     

黑斑狗鱼形态性状对体重的影响效果分析

选取0^＋龄～4^＋龄黑斑狗鱼332尾,对其体长、体高、体宽、尾柄长、尾柄高、头长、头高、头宽、吻长、眼间距、口裂宽、口裂长和体重等13个性状进行测量。采用多元分析法分析体重和形态形状的关系。结果表明,黑斑狗鱼除0^＋龄头长与体重之间的相关系数未达到显著水平（P〉0．05）外,其它各形态性状与体重的相关系数均达到显著水平（P〈0．05）或极显著水平（P〈0．01）;经通径分析,不同年龄阶段各形态性状对体重的通径系数存在一定的差异,但体长和头高对体重的通径系数均达到显著水平,而且体长对体重的直接作用均最大,其余各性状均是通过体长间接的影响体重;决定系数分析结果表明,体长对体重的决定系数最大,其它性状主要通过体长影响体重;通径分析结果与决定系数分析结果的变化趋势一致;所选形态性状与体重的相关指数R2〉0．85,说明所选性状是影响体重的主要性状。应用逐步多元回归分析,经偏回归系数的显著性检验,建立以体重为因变量（Y）,各形态性状为自变量的多元回归方程,0^＋龄～4^＋龄黑斑狗鱼体重（Y）与形态性状参数的多元叵归方程分别为：LgY=-1．411＋0．395LgX8＋0．576LgX1＋0．092LgX1n＋0．538LgX7＋0．35LgX6;LgY=-4．132＋0．866LgX1＋0．102LgX】2＋0．083LgX7;LgY=一4．186＋0．772LgX1＋0．195LgX7;LgY=-2．542＋0．372LgX1＋0．185LgX3＋0．256LgX8＋0．23LgX7,;LgY=-4．585＋0．816LgX1＋0．178LgX7。经回归预测估计值与实际值间的差异不显著（P〉0．05）,该方程可用于黑斑狗鱼实际生产中,为黑斑狗鱼选种提供了理论依据和理想的测量指标。     

Brown shrimp (Farfantepenaeus aztecus) density distribution in the Northern Gulf of Mexico: an approach using boosted regression trees

The estuarine‐dependent brown shrimp, Farfantepenaeus aztecus, is a significant commercial fishery and important species in the Gulf of Mexico (GOM) ecosystem as well as being a key component in energy transfer between benthic and pelagic food web systems. Because of the economical and ecological importance of brown shrimp, we developed a spatial population model to identify places of high shrimp density under a set of spatial, environmental and temporal variables in the Northern Gulf of Mexico (NGOM). We used fisheries‐independent data collected by the Southeast Area Monitoring and Assessment Program (SEAMAP) from 1992 to 2007 (summer and fall seasons). The relationship between the predictor variables and shrimp density was modeled using Boosted Regression Trees (BRT). Within the environmental variables included in the model, bottom type and depth of the water column were the most important predictors of shrimp density in the NGOM. Spatial predictions performed using the trained BRT model for summer and fall seasons showed a spatial segregation of shrimp density. During the summer, higher densities were predicted near the Texas and Louisiana coast and during the fall, higher densities were predicted further offshore. The model performed well and allowed successful prediction of brown shrimp hot spots in the NGOM. Model results allow fisheries managers to evaluate the potential impact from fisheries on the resource and to develop future fisheries management strategies, understand the biology of brown shrimp as well as assess the potential impacts of oil spills or climate change.     

应用栖息地指数对印度洋大眼金枪鱼分布模式研究

运用分位数回归方法对温度、温差、氧差与印度洋大眼金枪鱼延绳钓钓获率进行二次回归分析,找出最佳上界方程,以最佳上界方程拟合的数值来建立栖息地指数(HSI)模型,从而揭示印度洋大眼金枪鱼栖息地的分布模式。研究表明,温度、温差、氧差与印度洋大眼金枪鱼延绳钓钓获率的最佳上界分位数回归方程分别为HR_(T0.9)=-44.803 7.685T_(0.9)-0.255T_(0.9)~2,HR_(dT_(0.9))=6.234 0.953dT_(0.9)-0.026dT_(0.9)~2和HR_(dO_(0.88))=7.422 4.25dO_(0.88)-0.727dO_(0.88)~2。10°N～10°S间印度洋海域大眼金抢鱼HSI指数达到0.9以上;10°N以北的波斯湾及10°S～15°S海域的HSI指数为0.8～0.9;15°S～40°S之间海域HSI指数介于0.7～0.8,其中50°E～90°E、15°S～25°S间存在一片季节性HSI指数<0.7的区域;40°S以南的海域HSI指数<0.6。     

基于回归等值线法的不同下垫面红壤水分时空变化规律研究

基于回归等值线法对百喜草覆盖、百喜草敷盖和对照处理的红壤水分时空变化进行了比较分析。结果显示,从年初到年终,随时间的推移,各处理各层土壤含水量均表现出"先减小,后增大"的趋势。在测定深度内,随土层深度增加,各处理各层土壤含水量却表现出"先增大,后减小"的趋势。百喜草覆盖处理,特别是上中层土壤含水量,随时间变化较大,与气候和百喜草生长密切相关,但随深度变化,层间土壤含水量变化较慢;百喜草敷盖和对照处理,土壤含水量受土层深度因素影响更明显。对于百喜草敷盖处理,随深度变化,层间土壤含水量变化较慢,但对于对照处理,随深度变化,层间土壤含水量变化较大。     

Predicting the age of fish using general and generalized linear models of biometric data: A case study of two estuarine finfish from New South Wales, Australia

Direct ageing of fish can be a laborious and expensive task when age estimates from a large population are required, and often involves a degree of subjectivity. This study examined the application of general and generalized linear models that predict the age of fish from a range of efficiently and objectively measured covariates. The data sampled were from yellowfin bream (Acanthopagrus australis (Sparidae) (Owen, 1853)) and sand whiting (Sillago ciliata (Sillaginidae) Cuvier, 1829) populations from New South Wales, Australia. The covariates evaluated in the models were fish length, otolith weight, sex and location (the estuary from which the fish were sampled). Akaike Information Criteria were used for model selection and residual plots of the final models revealed a satisfactory fit to the observations. The best fitting model for each species included all covariates. An additional investigation considered whether general and generalized linear models that predict age from two different categories of biometric information outperform age-length keys with respect to subsequent estimates of total mortality from catch-curve analysis. The two categories of biometric information differed in the ease and cost with which the information could be collected. The first category only included fish length and location as covariates, whilst the second category also included otolith weight and sex. It was found that traditional age-length keys outperformed the predictive models that estimated age from only fish length and location, because the results from the models were prone to significant bias. However, when otolith weight and sex were added as covariates to the predictive models, some of them, including a generalized linear model with a Poisson-distributed response variable, performed similarly to the age-length key. Given that otolith weight and the sex of fish are cheaper to quantify than age from a sectioned otolith in many situations, general or generalized linear models may represent a cheaper and faster method of estimating mortality compared to age-length keys. Such models can also easily incorporate the influence of spatial, temporal and demographic variation.     

Application of the Tweedie distribution to zero-catch data in CPUE analysis

We focus on the zero-catch problem of CPUE (catch per unit effort) standardization. Because the traditional CPUE model with a log-normal error structure cannot be applied in this case, three methods have often been utilized as follows:

(1) Ad hoc method adds a small constant value to all response variables.

(2) Catch model with a Poisson or negative-binomial (NB) error structure.

(3) Delta-type two-step method such as the delta-normal model (after estimating the ratio of zero-catch using a logit or probit model, a model such as CPUE log-normal or Catch-Poisson is applied to CPUE without zero-data).

However, there are some statistical problems with each of these methods.In this paper, we carried out the CPUE standardization mainly using the Tweedie distribution model based on the actual by-catch data (silky shark, Carcharhimus falciformis, in the North Pacific Ocean caught by Japanese training vessels) including many observations with zero-catch (>2/3rd) and tuna fishery data as a target (yellowfin tuna, Thunnus albacares, in the Indian Ocean caught by Japanese commercial vessels) where the ratio of zero-catch is not so high (<1/3rd). The Tweedie model is an extension of compound Poisson model derived from the stochastic process where the weight of the counted objects (i.e., number of fish) has a gamma distribution and has an advantage of handling the zero-catch data in a unified way.We also compared four candidate models, the Catch-NB model, ad hoc method, Delta-lognormal model (delta-type two-step method) and Tweedie distribution, through CPUE analyses of actual fishery data in terms of the statistical performance. Square error and Pearson's correlation coefficient were calculated based on the observed CPUE and the corresponding predicted CPUE using the n-fold cross-validation.As a result, the differences in the trend of CPUE between years and model performance between the ad hoc method and Tweedie model were found to be not so large in the example of yellowfin tuna (target species). However, the statistical performance of Tweedie distribution is rather better than Delta-lognormal model, the Catch-NB distribution and ad hoc method in the example of silky shark (by-catch species). Standardized CPUE year trend of ad hoc method was found to be quite different from that of the Tweedie distribution and other two models. Model performance of the Tweedie distribution is good judging from the 5-fold cross-validation using the fishery data if including many zero-catch data such as by-catch species.     

Direct evidence of multiple paternities in natural population of viviparous Japanese surfperch by allelic markers of microsatellite DNA loci

This study was performed to obtain information on the occurrence of multiple paternities in three species of viviparous Japanese surfperch using allelic markers of microsatellite DNA loci. Direct evidence for multiple fertilizations was established by reconstructing paternal genotypes from the progeny of gravid females. Multiple paternities were ascertained in five of 10 broods of Ditrema temmincki and in three of nine broods of Neoditrema ransonneti, but not in Ditrema viride. The number of patrilines detected in the progeny of D. temmincki and N. ransonneti females were two or three, respectively, as determined by the GERUD v2.0 algorithm for reconstructing parental genotypes from half-sib progeny arrays.